When using a mobile telephone in a high-Doppler environment, (e.g. a Japanese bullet trains) in an OFDM system, there are OFDM symbol-to-symbol variations in channel coefficients. Channel estimation is necessary to detect data symbols in fading channels. During each data transmission (e.g. packet) in an OFDM system, there are some number of OFDM symbols that are transmitted. In high data rate, fading, high-Doppler scenarios expected in 4G, each individual symbol requires channel coefficient estimates, otherwise the detection performance is severely degraded to being unacceptable for a typical mobile user.
When estimating the channel characteristics in a fading, high-Doppler environment there are techniques that assume no particular underlying physical model for the channel coefficients. These are non-parametric approaches. There are also approaches that estimate channel coefficients based on underlying channel models that describe the channel in turn based on a set of parameters. These are parametric approaches. When the parametric models are very accurate in describing the underlying physical characteristics of the channel, then excellent results are obtained by exploiting the model.
Currently “Jake's model” (W. C. Jakes, Jr. Microwave Mobile Communications, John Wiley & Sons, pp. 75, 1974) is universally accepted as a way to parameterize the channel for fading. This model also captures the time-varying nature of Doppler channels in cellular environments.
However there are at least two problems that complicate channel estimation and prediction in high-Doppler user scenarios. One problem is the determination of the parameters of the model that are related to mobile speeds, since the model is used for tracking the channel. Predicting the next state of the channel, and thus exploiting the time-varying characteristics of the channel, reduces the channel estimation error. However this must be done accurately before any channel tracking takes place.
The second problem is the determination of a method to track the channel under time-varying conditions. Parametric approaches require the additional step of model estimation in order to exploit this knowledge to provide the expected channel tracking results. Furthermore this additional step usually requires additional overhead in a typical OFDM packet, which then reduces the amount of data-bearing traffic in each packet transmission and thus causes operator revenue loss.
Previous non-parametric approaches use technique such as least-squares to estimate channel coefficients using the OFDM packet header (i.e. pre-amble) and then apply these channel estimates for all OFDM symbols in a packet. The performance of this approach degrades when the channel coefficients change with time in high-Doppler environments.
Other approaches use embedded pilot symbols in each OFDM symbol, thus wasting spectral efficiency. Since no time-varying model is being used to predict the channel there is no reduction in channel coefficient error if that knowledge were available.
There are also non-parametric “blind” channel estimators that don't use data-bearing symbols in each OFDM symbol, but because there is no exploitation of the time-varying characteristics of the channel, there is no reduction in channel estimation error. There are parametric methods that estimate auto regressive (AR) parameters matched to Jakes model but this usually requires additional overhead to estimate the model. The models are error prone due to noisy data being used to estimate the model parameters. There are numerous other parametric methods that pick a particular channel coefficient model and then live with the results when confronted with an unknown channel.
Thus, the art has not yet developed a method of providing up to date channel parameters in a high-Doppler environment without excessive reduction of bandwidth.